mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-07-06 04:52:13 -07:00
added AMD/Intel cards support via DirectX12 ( DirectML backend )
This commit is contained in:
iperov
parent
fc4a49c3e7
commit
fdb143ff47
7 changed files with 166 additions and 116 deletions
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@ -1,12 +1,19 @@
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import sys
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import ctypes
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import os
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import multiprocessing
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import json
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import time
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from pathlib import Path
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from core.interact import interact as io
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class Device(object):
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def __init__(self, index, name, total_mem, free_mem, cc=0):
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def __init__(self, index, tf_dev_type, name, total_mem, free_mem):
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self.index = index
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self.tf_dev_type = tf_dev_type
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self.name = name
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self.cc = cc
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self.total_mem = total_mem
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self.total_mem_gb = total_mem / 1024**3
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self.free_mem = free_mem
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@ -82,12 +89,134 @@ class Devices(object):
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result.append (device)
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return Devices(result)
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@staticmethod
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def _get_tf_devices_proc(q : multiprocessing.Queue):
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compute_cache_path = Path(os.environ['APPDATA']) / 'NVIDIA' / ('ComputeCache_ALL')
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os.environ['CUDA_CACHE_PATH'] = str(compute_cache_path)
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if not compute_cache_path.exists():
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io.log_info("Caching GPU kernels...")
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compute_cache_path.mkdir(parents=True, exist_ok=True)
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import tensorflow
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tf_version = tensorflow.version.VERSION
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#if tf_version is None:
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# tf_version = tensorflow.version.GIT_VERSION
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if tf_version[0] == 'v':
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tf_version = tf_version[1:]
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if tf_version[0] == '2':
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tf = tensorflow.compat.v1
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else:
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tf = tensorflow
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import logging
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# Disable tensorflow warnings
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tf_logger = logging.getLogger('tensorflow')
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tf_logger.setLevel(logging.ERROR)
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from tensorflow.python.client import device_lib
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devices = []
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physical_devices = device_lib.list_local_devices()
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physical_devices_f = {}
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for dev in physical_devices:
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dev_type = dev.device_type
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dev_tf_name = dev.name
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dev_tf_name = dev_tf_name[ dev_tf_name.index(dev_type) : ]
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dev_idx = int(dev_tf_name.split(':')[-1])
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if dev_type in ['GPU','DML']:
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dev_name = dev_tf_name
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dev_desc = dev.physical_device_desc
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if len(dev_desc) != 0:
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if dev_desc[0] == '{':
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dev_desc_json = json.loads(dev_desc)
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dev_desc_json_name = dev_desc_json.get('name',None)
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if dev_desc_json_name is not None:
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dev_name = dev_desc_json_name
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else:
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for param, value in ( v.split(':') for v in dev_desc.split(',') ):
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param = param.strip()
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value = value.strip()
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if param == 'name':
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dev_name = value
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break
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physical_devices_f[dev_idx] = (dev_type, dev_name, dev.memory_limit)
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q.put(physical_devices_f)
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time.sleep(0.1)
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@staticmethod
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def initialize_main_env():
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os.environ['NN_DEVICES_INITIALIZED'] = '1'
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os.environ['NN_DEVICES_COUNT'] = '0'
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if int(os.environ.get("NN_DEVICES_INITIALIZED", 0)) != 0:
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return
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if 'CUDA_VISIBLE_DEVICES' in os.environ.keys():
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os.environ.pop('CUDA_VISIBLE_DEVICES')
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os.environ['CUDA_CACHE_MAXSIZE'] = '2147483647'
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os.environ['TF_MIN_GPU_MULTIPROCESSOR_COUNT'] = '2'
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # tf log errors only
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q = multiprocessing.Queue()
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p = multiprocessing.Process(target=Devices._get_tf_devices_proc, args=(q,), daemon=True)
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p.start()
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p.join()
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visible_devices = q.get()
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os.environ['NN_DEVICES_INITIALIZED'] = '1'
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os.environ['NN_DEVICES_COUNT'] = str(len(visible_devices))
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for i in visible_devices:
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dev_type, name, total_mem = visible_devices[i]
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os.environ[f'NN_DEVICE_{i}_TF_DEV_TYPE'] = dev_type
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os.environ[f'NN_DEVICE_{i}_NAME'] = name
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os.environ[f'NN_DEVICE_{i}_TOTAL_MEM'] = str(total_mem)
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os.environ[f'NN_DEVICE_{i}_FREE_MEM'] = str(total_mem)
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@staticmethod
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def getDevices():
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if Devices.all_devices is None:
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if int(os.environ.get("NN_DEVICES_INITIALIZED", 0)) != 1:
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raise Exception("nn devices are not initialized. Run initialize_main_env() in main process.")
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devices = []
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for i in range ( int(os.environ['NN_DEVICES_COUNT']) ):
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devices.append ( Device(index=i,
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tf_dev_type=os.environ[f'NN_DEVICE_{i}_TF_DEV_TYPE'],
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name=os.environ[f'NN_DEVICE_{i}_NAME'],
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total_mem=int(os.environ[f'NN_DEVICE_{i}_TOTAL_MEM']),
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free_mem=int(os.environ[f'NN_DEVICE_{i}_FREE_MEM']), )
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)
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Devices.all_devices = Devices(devices)
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return Devices.all_devices
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"""
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# {'name' : name.split(b'\0', 1)[0].decode(),
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# 'total_mem' : totalMem.value
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# }
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return
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min_cc = int(os.environ.get("TF_MIN_REQ_CAP", 35))
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libnames = ('libcuda.so', 'libcuda.dylib', 'nvcuda.dll')
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for libname in libnames:
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@ -139,70 +268,4 @@ class Devices(object):
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os.environ[f'NN_DEVICE_{i}_TOTAL_MEM'] = str(device['total_mem'])
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os.environ[f'NN_DEVICE_{i}_FREE_MEM'] = str(device['free_mem'])
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os.environ[f'NN_DEVICE_{i}_CC'] = str(device['cc'])
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@staticmethod
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def getDevices():
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if Devices.all_devices is None:
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if int(os.environ.get("NN_DEVICES_INITIALIZED", 0)) != 1:
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raise Exception("nn devices are not initialized. Run initialize_main_env() in main process.")
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devices = []
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for i in range ( int(os.environ['NN_DEVICES_COUNT']) ):
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devices.append ( Device(index=i,
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name=os.environ[f'NN_DEVICE_{i}_NAME'],
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total_mem=int(os.environ[f'NN_DEVICE_{i}_TOTAL_MEM']),
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free_mem=int(os.environ[f'NN_DEVICE_{i}_FREE_MEM']),
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cc=int(os.environ[f'NN_DEVICE_{i}_CC']) ))
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Devices.all_devices = Devices(devices)
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return Devices.all_devices
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"""
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if Devices.all_devices is None:
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min_cc = int(os.environ.get("TF_MIN_REQ_CAP", 35))
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libnames = ('libcuda.so', 'libcuda.dylib', 'nvcuda.dll')
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for libname in libnames:
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try:
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cuda = ctypes.CDLL(libname)
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except:
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continue
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else:
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break
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else:
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return Devices([])
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nGpus = ctypes.c_int()
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name = b' ' * 200
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cc_major = ctypes.c_int()
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cc_minor = ctypes.c_int()
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freeMem = ctypes.c_size_t()
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totalMem = ctypes.c_size_t()
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result = ctypes.c_int()
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device = ctypes.c_int()
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context = ctypes.c_void_p()
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error_str = ctypes.c_char_p()
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devices = []
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if cuda.cuInit(0) == 0 and \
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cuda.cuDeviceGetCount(ctypes.byref(nGpus)) == 0:
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for i in range(nGpus.value):
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if cuda.cuDeviceGet(ctypes.byref(device), i) != 0 or \
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cuda.cuDeviceGetName(ctypes.c_char_p(name), len(name), device) != 0 or \
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cuda.cuDeviceComputeCapability(ctypes.byref(cc_major), ctypes.byref(cc_minor), device) != 0:
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continue
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if cuda.cuCtxCreate_v2(ctypes.byref(context), 0, device) == 0:
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if cuda.cuMemGetInfo_v2(ctypes.byref(freeMem), ctypes.byref(totalMem)) == 0:
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cc = cc_major.value * 10 + cc_minor.value
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if cc >= min_cc:
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devices.append ( Device(index=i,
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name=name.split(b'\0', 1)[0].decode(),
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total_mem=totalMem.value,
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free_mem=freeMem.value,
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cc=cc) )
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cuda.cuCtxDetach(context)
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Devices.all_devices = Devices(devices)
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return Devices.all_devices
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"""
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@ -33,8 +33,8 @@ class nn():
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tf = None
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tf_sess = None
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tf_sess_config = None
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tf_default_device = None
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tf_default_device_name = None
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data_format = None
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conv2d_ch_axis = None
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conv2d_spatial_axes = None
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@ -50,9 +50,6 @@ class nn():
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nn.setCurrentDeviceConfig(device_config)
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# Manipulate environment variables before import tensorflow
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if 'CUDA_VISIBLE_DEVICES' in os.environ.keys():
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os.environ.pop('CUDA_VISIBLE_DEVICES')
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first_run = False
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if len(device_config.devices) != 0:
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@ -68,22 +65,19 @@ class nn():
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compute_cache_path = Path(os.environ['APPDATA']) / 'NVIDIA' / ('ComputeCache' + devices_str)
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if not compute_cache_path.exists():
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first_run = True
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compute_cache_path.mkdir(parents=True, exist_ok=True)
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os.environ['CUDA_CACHE_PATH'] = str(compute_cache_path)
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os.environ['TF_MIN_GPU_MULTIPROCESSOR_COUNT'] = '2'
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # tf log errors only
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if first_run:
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io.log_info("Caching GPU kernels...")
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import tensorflow
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tf_version = getattr(tensorflow,'VERSION', None)
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if tf_version is None:
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tf_version = tensorflow.version.GIT_VERSION
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if tf_version[0] == 'v':
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tf_version = tf_version[1:]
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tf_version = tensorflow.version.VERSION
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#if tf_version is None:
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# tf_version = tensorflow.version.GIT_VERSION
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if tf_version[0] == 'v':
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tf_version = tf_version[1:]
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if tf_version[0] == '2':
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tf = tensorflow.compat.v1
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else:
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@ -108,13 +102,14 @@ class nn():
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# Configure tensorflow session-config
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if len(device_config.devices) == 0:
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nn.tf_default_device = "/CPU:0"
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config = tf.ConfigProto(device_count={'GPU': 0})
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nn.tf_default_device_name = '/CPU:0'
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else:
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nn.tf_default_device = "/GPU:0"
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nn.tf_default_device_name = f'/{device_config.devices[0].tf_dev_type}:0'
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config = tf.ConfigProto()
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config.gpu_options.visible_device_list = ','.join([str(device.index) for device in device_config.devices])
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config.gpu_options.force_gpu_compatible = True
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config.gpu_options.allow_growth = True
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nn.tf_sess_config = config
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@ -202,14 +197,6 @@ class nn():
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nn.tf_sess.close()
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nn.tf_sess = None
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@staticmethod
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def get_current_device():
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# Undocumented access to last tf.device(...)
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objs = nn.tf.get_default_graph()._device_function_stack.peek_objs()
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if len(objs) != 0:
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return objs[0].display_name
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return nn.tf_default_device
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@staticmethod
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def ask_choose_device_idxs(choose_only_one=False, allow_cpu=True, suggest_best_multi_gpu=False, suggest_all_gpu=False):
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devices = Devices.getDevices()
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